Classifying Wireless Siblings of the LoRa Family, Radio Frequency Fingerprint Identification using Deep Learning

Overview

  • Funder: Royal Society
  • Scheme: Royal Society Research Grants
  • Duration: March 2019 to March 2020
  • Amount: £19k

Introduction

Low power wide area network (LPWAN) is a key enabler of the Internet of Things (IoT) to connect sensors cheaply and reliably for smart manufacturing, agriculture, healthcare, etc. McKinsey estimated that LPWAN would deliver £19.2b to £57.6b for the UK by 2025. LoRa is a key technology for the LPWAN with 0.6 billion devices estimated by 2023. The UK is ambitious to build a national LoRa network.

The wireless security is vital because the exchanged data may be confidential. In particular, device authentication is very essential for allowing the legitimate users to access the network but more important for rejecting the malicious attackers. Conventional schemes use device address such as MAC address which can be easily tampered even by amateurs. This is even worse for LoRa because many LoRa sensors are low cost with limited resources. On the other hand, a LoRa gateway (base station) can connect thousands of end devices and access of malicious users may compromise the entire LoRa network.

This project aims to design a robust device authentication scheme by exploiting the radio frequency fingerprinting (RFF). RFF is intrinsic hardware imperfection resulted from the manufacturing process, which is unique and cannot be tampered. Therefore, it is a good candidate for device identity.

Methodology

RFF identification is consisted of two stages, namely the training and classification.

RFFI for LoRa
RFF identification for LoRa

Outcome

Supported by this Research Grants, our group has equipped with LoRa devices from different vendors (Pycom FiPy and LoPy4, Semtech kits) and software defined radio (SDR) platforms (including USRP N210, PlutoSDR, RTLSDR, etc). We have built LoRa-RFFI testbed and achieved fruitful outcomes.

Publication

  1. 2024
    G. Shen, J. Zhang, A. Marshall, R. Woods, J. Cavallaro, and L. Chen, “Towards receiver-agnostic and collaborative radio frequency fingerprint identification,” IEEE Transactions on Mobile Computing, vol. 23, no. 7, pp. 7618–7634, 2024, doi: 10.1109/tmc.2023.3340039.
    DOI arXiv Code Data 
    @article{shen2023towards,
  title = {Towards receiver-agnostic and collaborative radio frequency fingerprint identification},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Woods, Roger and Cavallaro, Joseph and Chen, Liquan},
  journal = {IEEE Transactions on Mobile Computing},
  volume = {23},
  number = {7},
  pages = {7618 - 7634},
  year = {2024},
  doi = {10.1109/tmc.2023.3340039},
  arxiv = {2207.02999},
  keywords = {rffi,lora, data_code}
}
  2. 2023
    G. Shen, J. Zhang, A. Marshall, M. Valkama, and J. R. Cavallaro, “Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification,” IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2355–2367, 2023, doi: 10.1109/tifs.2023.3266626.
    DOI arXiv Code Data 
    @article{shen2023length,
  title = {Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Valkama, Mikko and Cavallaro, Joseph R},
  journal = {IEEE Transactions on Information Forensics and Security},
  volume = {18},
  pages = {2355 - 2367},
  year = {2023},
  doi = {10.1109/tifs.2023.3266626},
  arxiv = {2207.03001},
  keywords = {rffi,lora, data_code}
}
  3. 2022
    G. Shen, J. Zhang, A. Marshall, and J. R. Cavallaro, “Towards scalable and channel-robust radio frequency fingerprint identification for LoRa,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 774–787, 2022, doi: 10.1109/tifs.2022.3152404.
    DOI arXiv Code Data 
    @article{shen2022scalable,
  title = {Towards scalable and channel-robust radio frequency fingerprint identification for {LoRa}},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Cavallaro, Joseph R},
  journal = {IEEE Transactions on Information Forensics and Security},
  volume = {17},
  pages = {774--787},
  year = {2022},
  doi = {10.1109/tifs.2022.3152404},
  arxiv = {2107.02867},
  keywords = {rffi,lora,data_code}
}
  4. 2021
    G. Shen, J. Zhang, A. Marshall, L. Peng, and X. Wang, “Radio frequency fingerprint identification for LoRa using spectrogram and CNN,” in Proc. IEEE Conference on Computer Communications (INFOCOM), IEEE, 2021, pp. 1–10. doi: 10.1109/infocom42981.2021.9488793.
    DOI arXiv 
    @inproceedings{shen2021infocom,
  title = {Radio frequency fingerprint identification for LoRa using spectrogram and {CNN}},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Peng, Linning and Wang, Xianbin},
  booktitle = {Proc. IEEE Conference on Computer Communications (INFOCOM)},
  pages = {1--10},
  year = {2021},
  doi = {10.1109/infocom42981.2021.9488793},
  arxiv = {2101.01668},
  organization = {IEEE},
  keywords = {rffi,lora}
}
  5. 2021
    G. Shen, J. Zhang, A. Marshall, L. Peng, and X. Wang, “Radio frequency fingerprint identification for LoRa using deep learning,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2604–2616, 2021, doi: 10.1109/jsac.2021.3087250.
    DOI 
    @article{shen2021radioj,
  title = {Radio frequency fingerprint identification for {LoRa} using deep learning},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Peng, Linning and Wang, Xianbin},
  journal = {IEEE Journal on Selected Areas in Communications},
  volume = {39},
  number = {8},
  pages = {2604--2616},
  year = {2021},
  doi = {10.1109/jsac.2021.3087250},
  keywords = {rffi,lora}
}

Datasets and Code

The datasets and source code associated with the following papers are publicly available.

  1. 2024
    G. Shen, J. Zhang, A. Marshall, R. Woods, J. Cavallaro, and L. Chen, “Towards receiver-agnostic and collaborative radio frequency fingerprint identification,” IEEE Transactions on Mobile Computing, vol. 23, no. 7, pp. 7618–7634, 2024, doi: 10.1109/tmc.2023.3340039.
    DOI arXiv Code Data 
    @article{shen2023towards,
  title = {Towards receiver-agnostic and collaborative radio frequency fingerprint identification},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Woods, Roger and Cavallaro, Joseph and Chen, Liquan},
  journal = {IEEE Transactions on Mobile Computing},
  volume = {23},
  number = {7},
  pages = {7618 - 7634},
  year = {2024},
  doi = {10.1109/tmc.2023.3340039},
  arxiv = {2207.02999},
  keywords = {rffi,lora, data_code}
}
  2. 2024
    G. Shen, J. Zhang, X. Wang, and S. Mao, “Federated Radio Frequency Fingerprint Identification Powered by Unsupervised Contrastive Learning,” IEEE Transactions on Information Forensics and Security, vol. 19, pp. 9204–9215, 2024, doi: 10.1109/tifs.2024.3469820.
    DOI Code Data 
    @article{shen2024federated,
  title = {Federated Radio Frequency Fingerprint Identification Powered by Unsupervised Contrastive Learning},
  author = {Shen, Guanxiong and Zhang, Junqing and Wang, Xuyu and Mao, Shiwen},
  journal = {IEEE Transactions on Information Forensics and Security},
  volume = {19},
  pages = {9204 - 9215},
  year = {2024},
  doi = {10.1109/tifs.2024.3469820},
  keywords = {rffi, lora, data_code}
}
  3. 2023
    G. Shen, J. Zhang, A. Marshall, M. Valkama, and J. R. Cavallaro, “Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification,” IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2355–2367, 2023, doi: 10.1109/tifs.2023.3266626.
    DOI arXiv Code Data 
    @article{shen2023length,
  title = {Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Valkama, Mikko and Cavallaro, Joseph R},
  journal = {IEEE Transactions on Information Forensics and Security},
  volume = {18},
  pages = {2355 - 2367},
  year = {2023},
  doi = {10.1109/tifs.2023.3266626},
  arxiv = {2207.03001},
  keywords = {rffi,lora, data_code}
}
  4. 2022
    G. Shen, J. Zhang, A. Marshall, and J. R. Cavallaro, “Towards scalable and channel-robust radio frequency fingerprint identification for LoRa,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 774–787, 2022, doi: 10.1109/tifs.2022.3152404.
    DOI arXiv Code Data 
    @article{shen2022scalable,
  title = {Towards scalable and channel-robust radio frequency fingerprint identification for {LoRa}},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Cavallaro, Joseph R},
  journal = {IEEE Transactions on Information Forensics and Security},
  volume = {17},
  pages = {774--787},
  year = {2022},
  doi = {10.1109/tifs.2022.3152404},
  arxiv = {2107.02867},
  keywords = {rffi,lora,data_code}
}